An AI Image Fabrication detector has been developed to identify AI-generated images in scientific publications. The tool claims a 98% success rate with a 0.02% false-positive rate and can detect AI-generated microscopy images. Science journal is using the tool, while Springer Nature is developing its own detection tools. Regular updates are planned to recognise images from new AI models.

**For scientific publishers and research institutions:** You'll need to integrate AI detection tools into your review processes. This will increase operational costs but enhance your reputation for integrity. Consider partnering with tool developers or investing in in-house solutions. **For AI companies:** You're facing a new market opportunity. Develop and market AI detection tools to scientific publishers, research institutions, and regulatory bodies. Focus on improving accuracy and reducing false positives to gain a competitive edge. **For researchers and academics:** You'll need to adapt your workflows. Ensure all images in your publications can pass AI detection scrutiny. This might slow down your research process initially but will ultimately strengthen the credibility of your work.

Expect an arms race between AI image generators and detectors. As detection tools improve, so will the sophistication of AI-generated images. This cycle will drive rapid innovation in both fields. Anticipate increased regulatory scrutiny. Governments and scientific bodies may mandate the use of AI detection tools in peer-review processes. This could lead to new compliance requirements for publishers and researchers. Watch for the emergence of 'AI authenticity' as a marketable feature. Tools that can prove the authenticity of non-AI images may become valuable assets across various industries, from journalism to legal evidence.